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Commit 1. Lab1 done
2024-10-25 17:41:51 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Лабораторная работа №1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Датасет 12. Цены на акции Starbucks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) Загрузка и сохранение данных"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>224358400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>58732800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>34777600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-07-01</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.339844</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>18316800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-07-02</td>\n",
" <td>0.359375</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>13996800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close Volume\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938 0.260703 224358400\n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 58732800\n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 34777600\n",
"3 1992-07-01 0.351563 0.359375 0.339844 0.355469 0.275860 18316800\n",
"4 1992-07-02 0.359375 0.359375 0.347656 0.355469 0.275860 13996800"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"coffee.csv\")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
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" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" <td>80.720001</td>\n",
" <td>22063400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" <td>79.260002</td>\n",
" <td>4651418</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume \n",
"8034 22063400 \n",
"8035 4651418 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail(2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"newCoffee.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) Получение сведений о датафрейме с данными"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8036.000000</td>\n",
" <td>8036.000000</td>\n",
" <td>8036.000000</td>\n",
" <td>8036.000000</td>\n",
" <td>8036.000000</td>\n",
" <td>8.036000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>30.054280</td>\n",
" <td>30.351487</td>\n",
" <td>29.751322</td>\n",
" <td>30.058857</td>\n",
" <td>26.674025</td>\n",
" <td>1.470459e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>33.615577</td>\n",
" <td>33.906613</td>\n",
" <td>33.314569</td>\n",
" <td>33.615911</td>\n",
" <td>31.728090</td>\n",
" <td>1.340021e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>1.504000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.392031</td>\n",
" <td>4.531250</td>\n",
" <td>4.304922</td>\n",
" <td>4.399610</td>\n",
" <td>3.414300</td>\n",
" <td>7.817750e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.325000</td>\n",
" <td>13.493750</td>\n",
" <td>13.150000</td>\n",
" <td>13.330000</td>\n",
" <td>10.352452</td>\n",
" <td>1.169815e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>55.250000</td>\n",
" <td>55.722501</td>\n",
" <td>54.852499</td>\n",
" <td>55.267499</td>\n",
" <td>47.464829</td>\n",
" <td>1.778795e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>126.080002</td>\n",
" <td>126.320000</td>\n",
" <td>124.809998</td>\n",
" <td>126.059998</td>\n",
" <td>118.010414</td>\n",
" <td>5.855088e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Adj Close \\\n",
"count 8036.000000 8036.000000 8036.000000 8036.000000 8036.000000 \n",
"mean 30.054280 30.351487 29.751322 30.058857 26.674025 \n",
"std 33.615577 33.906613 33.314569 33.615911 31.728090 \n",
"min 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"25% 4.392031 4.531250 4.304922 4.399610 3.414300 \n",
"50% 13.325000 13.493750 13.150000 13.330000 10.352452 \n",
"75% 55.250000 55.722501 54.852499 55.267499 47.464829 \n",
"max 126.080002 126.320000 124.809998 126.059998 118.010414 \n",
"\n",
" Volume \n",
"count 8.036000e+03 \n",
"mean 1.470459e+07 \n",
"std 1.340021e+07 \n",
"min 1.504000e+06 \n",
"25% 7.817750e+06 \n",
"50% 1.169815e+07 \n",
"75% 1.778795e+07 \n",
"max 5.855088e+08 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8036 entries, 0 to 8035\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Date 8036 non-null object \n",
" 1 Open 8036 non-null float64\n",
" 2 High 8036 non-null float64\n",
" 3 Low 8036 non-null float64\n",
" 4 Close 8036 non-null float64\n",
" 5 Adj Close 8036 non-null float64\n",
" 6 Volume 8036 non-null int64 \n",
"dtypes: float64(5), int64(1), object(1)\n",
"memory usage: 439.6+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Получение сведений о колонках датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Вывод отдельных строк и столбцов из датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Open</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.328125</td>\n",
" <td>0.335938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.339844</td>\n",
" <td>0.359375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.367188</td>\n",
" <td>0.347656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.351563</td>\n",
" <td>0.355469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.359375</td>\n",
" <td>0.355469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>75.269997</td>\n",
" <td>77.849998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>77.680000</td>\n",
" <td>77.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>77.559998</td>\n",
" <td>77.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>77.699997</td>\n",
" <td>80.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>80.099998</td>\n",
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"<p>8036 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Open Close\n",
"0 0.328125 0.335938\n",
"1 0.339844 0.359375\n",
"2 0.367188 0.347656\n",
"3 0.351563 0.355469\n",
"4 0.359375 0.355469\n",
"... ... ...\n",
"8031 75.269997 77.849998\n",
"8032 77.680000 77.540001\n",
"8033 77.559998 77.720001\n",
"8034 77.699997 80.720001\n",
"8035 80.099998 79.260002\n",
"\n",
"[8036 rows x 2 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"Open\", \"Close\"]]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
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" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1992-07-06</td>\n",
" <td>0.351563</td>\n",
" <td>0.355469</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>5753600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1992-07-07</td>\n",
" <td>0.355469</td>\n",
" <td>0.355469</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>10662400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1992-07-08</td>\n",
" <td>0.355469</td>\n",
" <td>0.355469</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>15500800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1992-07-09</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>3923200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1992-07-10</td>\n",
" <td>0.359375</td>\n",
" <td>0.367188</td>\n",
" <td>0.351563</td>\n",
" <td>0.363281</td>\n",
" <td>0.281923</td>\n",
" <td>11040000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close Volume\n",
"5 1992-07-06 0.351563 0.355469 0.347656 0.355469 0.275860 5753600\n",
"6 1992-07-07 0.355469 0.355469 0.347656 0.355469 0.275860 10662400\n",
"7 1992-07-08 0.355469 0.355469 0.343750 0.347656 0.269797 15500800\n",
"8 1992-07-09 0.351563 0.359375 0.347656 0.359375 0.278891 3923200\n",
"9 1992-07-10 0.359375 0.367188 0.351563 0.363281 0.281923 11040000"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[5:10]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
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" <tr>\n",
" <th>7322</th>\n",
" <td>2021-07-23</td>\n",
" <td>124.550003</td>\n",
" <td>126.320000</td>\n",
" <td>123.919998</td>\n",
" <td>125.970001</td>\n",
" <td>117.926170</td>\n",
" <td>7934200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7323</th>\n",
" <td>2021-07-26</td>\n",
" <td>125.739998</td>\n",
" <td>126.099998</td>\n",
" <td>124.250000</td>\n",
" <td>126.059998</td>\n",
" <td>118.010414</td>\n",
" <td>4827500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7324</th>\n",
" <td>2021-07-27</td>\n",
" <td>126.080002</td>\n",
" <td>126.160004</td>\n",
" <td>124.809998</td>\n",
" <td>126.029999</td>\n",
" <td>117.982330</td>\n",
" <td>6110900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7325</th>\n",
" <td>2021-07-28</td>\n",
" <td>122.559998</td>\n",
" <td>123.330002</td>\n",
" <td>121.389999</td>\n",
" <td>122.410004</td>\n",
" <td>114.593483</td>\n",
" <td>11747000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7326</th>\n",
" <td>2021-07-29</td>\n",
" <td>122.930000</td>\n",
" <td>123.470001</td>\n",
" <td>122.139999</td>\n",
" <td>122.379997</td>\n",
" <td>114.565414</td>\n",
" <td>6618400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7327</th>\n",
" <td>2021-07-30</td>\n",
" <td>122.190002</td>\n",
" <td>122.980003</td>\n",
" <td>121.099998</td>\n",
" <td>121.430000</td>\n",
" <td>113.676071</td>\n",
" <td>5712300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7328</th>\n",
" <td>2021-08-02</td>\n",
" <td>122.029999</td>\n",
" <td>122.980003</td>\n",
" <td>120.070000</td>\n",
" <td>120.370003</td>\n",
" <td>112.683769</td>\n",
" <td>5996800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7329</th>\n",
" <td>2021-08-03</td>\n",
" <td>120.570000</td>\n",
" <td>120.750000</td>\n",
" <td>117.519997</td>\n",
" <td>119.129997</td>\n",
" <td>111.522942</td>\n",
" <td>6030500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"7322 2021-07-23 124.550003 126.320000 123.919998 125.970001 117.926170 \n",
"7323 2021-07-26 125.739998 126.099998 124.250000 126.059998 118.010414 \n",
"7324 2021-07-27 126.080002 126.160004 124.809998 126.029999 117.982330 \n",
"7325 2021-07-28 122.559998 123.330002 121.389999 122.410004 114.593483 \n",
"7326 2021-07-29 122.930000 123.470001 122.139999 122.379997 114.565414 \n",
"7327 2021-07-30 122.190002 122.980003 121.099998 121.430000 113.676071 \n",
"7328 2021-08-02 122.029999 122.980003 120.070000 120.370003 112.683769 \n",
"7329 2021-08-03 120.570000 120.750000 117.519997 119.129997 111.522942 \n",
"\n",
" Volume \n",
"7322 7934200 \n",
"7323 4827500 \n",
"7324 6110900 \n",
"7325 11747000 \n",
"7326 6618400 \n",
"7327 5712300 \n",
"7328 5996800 \n",
"7329 6030500 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Open'] > 120]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. Группировка и агрегация данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Low</th>\n",
" </tr>\n",
" <tr>\n",
" <th>High</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.347656</th>\n",
" <td>0.320313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.355469</th>\n",
" <td>0.346354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.359375</th>\n",
" <td>0.345052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.367188</th>\n",
" <td>0.341797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.371094</th>\n",
" <td>0.351562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123.330002</th>\n",
" <td>121.389999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123.470001</th>\n",
" <td>122.139999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126.099998</th>\n",
" <td>124.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126.160004</th>\n",
" <td>124.809998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126.320000</th>\n",
" <td>123.919998</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5245 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Low\n",
"High \n",
"0.347656 0.320313\n",
"0.355469 0.346354\n",
"0.359375 0.345052\n",
"0.367188 0.341797\n",
"0.371094 0.351562\n",
"... ...\n",
"123.330002 121.389999\n",
"123.470001 122.139999\n",
"126.099998 124.250000\n",
"126.160004 124.809998\n",
"126.320000 123.919998\n",
"\n",
"[5245 rows x 1 columns]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group = df.groupby(['High'])['Low'].mean()\n",
"group.to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. Сортировка данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
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" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>224358400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>58732800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>34777600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-07-01</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.339844</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>18316800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-07-02</td>\n",
" <td>0.359375</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>13996800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>2024-05-17</td>\n",
" <td>75.269997</td>\n",
" <td>78.000000</td>\n",
" <td>74.919998</td>\n",
" <td>77.849998</td>\n",
" <td>77.849998</td>\n",
" <td>14436500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>2024-05-20</td>\n",
" <td>77.680000</td>\n",
" <td>78.320000</td>\n",
" <td>76.709999</td>\n",
" <td>77.540001</td>\n",
" <td>77.540001</td>\n",
" <td>11183800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>2024-05-21</td>\n",
" <td>77.559998</td>\n",
" <td>78.220001</td>\n",
" <td>77.500000</td>\n",
" <td>77.720001</td>\n",
" <td>77.720001</td>\n",
" <td>8916600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" <td>80.720001</td>\n",
" <td>22063400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" <td>79.260002</td>\n",
" <td>4651418</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8036 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 \n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 \n",
"3 1992-07-01 0.351563 0.359375 0.339844 0.355469 0.275860 \n",
"4 1992-07-02 0.359375 0.359375 0.347656 0.355469 0.275860 \n",
"... ... ... ... ... ... ... \n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998 77.849998 \n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001 77.540001 \n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001 77.720001 \n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume \n",
"0 224358400 \n",
"1 58732800 \n",
"2 34777600 \n",
"3 18316800 \n",
"4 13996800 \n",
"... ... \n",
"8031 14436500 \n",
"8032 11183800 \n",
"8033 8916600 \n",
"8034 22063400 \n",
"8035 4651418 \n",
"\n",
"[8036 rows x 7 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_df = df.sort_values(by='Date', ascending = True)\n",
"sorted_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"7. Удаление строк/столбцов"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"df_dropped_columns = df.drop(columns=['Adj Close', 'Volume']) # Удаление столбцов 'Adj Close' и 'Volume'"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
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" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-07-01</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.339844</td>\n",
" <td>0.355469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-07-02</td>\n",
" <td>0.359375</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>2024-05-17</td>\n",
" <td>75.269997</td>\n",
" <td>78.000000</td>\n",
" <td>74.919998</td>\n",
" <td>77.849998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>2024-05-20</td>\n",
" <td>77.680000</td>\n",
" <td>78.320000</td>\n",
" <td>76.709999</td>\n",
" <td>77.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>2024-05-21</td>\n",
" <td>77.559998</td>\n",
" <td>78.220001</td>\n",
" <td>77.500000</td>\n",
" <td>77.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8036 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938\n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375\n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656\n",
"3 1992-07-01 0.351563 0.359375 0.339844 0.355469\n",
"4 1992-07-02 0.359375 0.359375 0.347656 0.355469\n",
"... ... ... ... ... ...\n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998\n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001\n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001\n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001\n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002\n",
"\n",
"[8036 rows x 5 columns]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_columns"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>224358400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>58732800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>34777600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1992-07-06</td>\n",
" <td>0.351563</td>\n",
" <td>0.355469</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>5753600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1992-07-07</td>\n",
" <td>0.355469</td>\n",
" <td>0.355469</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>10662400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>2024-05-17</td>\n",
" <td>75.269997</td>\n",
" <td>78.000000</td>\n",
" <td>74.919998</td>\n",
" <td>77.849998</td>\n",
" <td>77.849998</td>\n",
" <td>14436500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>2024-05-20</td>\n",
" <td>77.680000</td>\n",
" <td>78.320000</td>\n",
" <td>76.709999</td>\n",
" <td>77.540001</td>\n",
" <td>77.540001</td>\n",
" <td>11183800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>2024-05-21</td>\n",
" <td>77.559998</td>\n",
" <td>78.220001</td>\n",
" <td>77.500000</td>\n",
" <td>77.720001</td>\n",
" <td>77.720001</td>\n",
" <td>8916600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" <td>80.720001</td>\n",
" <td>22063400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" <td>79.260002</td>\n",
" <td>4651418</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8034 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 \n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 \n",
"5 1992-07-06 0.351563 0.355469 0.347656 0.355469 0.275860 \n",
"6 1992-07-07 0.355469 0.355469 0.347656 0.355469 0.275860 \n",
"... ... ... ... ... ... ... \n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998 77.849998 \n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001 77.540001 \n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001 77.720001 \n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume \n",
"0 224358400 \n",
"1 58732800 \n",
"2 34777600 \n",
"5 5753600 \n",
"6 10662400 \n",
"... ... \n",
"8031 14436500 \n",
"8032 11183800 \n",
"8033 8916600 \n",
"8034 22063400 \n",
"8035 4651418 \n",
"\n",
"[8034 rows x 7 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_rows = df.drop([3, 4]) # Удаление строк с индексами 3 и 4\n",
"df_dropped_rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"8. Создание новых столбцов на основе данных из существующих столбцов датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"df['Difference'] = df['High'] - df['Low']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>224358400</td>\n",
" <td>0.027343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>58732800</td>\n",
" <td>0.035157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>34777600</td>\n",
" <td>0.027344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-07-01</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.339844</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>18316800</td>\n",
" <td>0.019531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-07-02</td>\n",
" <td>0.359375</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>13996800</td>\n",
" <td>0.011719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>2024-05-17</td>\n",
" <td>75.269997</td>\n",
" <td>78.000000</td>\n",
" <td>74.919998</td>\n",
" <td>77.849998</td>\n",
" <td>77.849998</td>\n",
" <td>14436500</td>\n",
" <td>3.080002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>2024-05-20</td>\n",
" <td>77.680000</td>\n",
" <td>78.320000</td>\n",
" <td>76.709999</td>\n",
" <td>77.540001</td>\n",
" <td>77.540001</td>\n",
" <td>11183800</td>\n",
" <td>1.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>2024-05-21</td>\n",
" <td>77.559998</td>\n",
" <td>78.220001</td>\n",
" <td>77.500000</td>\n",
" <td>77.720001</td>\n",
" <td>77.720001</td>\n",
" <td>8916600</td>\n",
" <td>0.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" <td>80.720001</td>\n",
" <td>22063400</td>\n",
" <td>3.579995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" <td>79.260002</td>\n",
" <td>4651418</td>\n",
" <td>1.529999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8036 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 \n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 \n",
"3 1992-07-01 0.351563 0.359375 0.339844 0.355469 0.275860 \n",
"4 1992-07-02 0.359375 0.359375 0.347656 0.355469 0.275860 \n",
"... ... ... ... ... ... ... \n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998 77.849998 \n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001 77.540001 \n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001 77.720001 \n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume Difference \n",
"0 224358400 0.027343 \n",
"1 58732800 0.035157 \n",
"2 34777600 0.027344 \n",
"3 18316800 0.019531 \n",
"4 13996800 0.011719 \n",
"... ... ... \n",
"8031 14436500 3.080002 \n",
"8032 11183800 1.610001 \n",
"8033 8916600 0.720001 \n",
"8034 22063400 3.579995 \n",
"8035 4651418 1.529999 \n",
"\n",
"[8036 rows x 8 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9. Удаление строк с пустыми значениями"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Date 0\n",
"Open 0\n",
"High 0\n",
"Low 0\n",
"Close 0\n",
"Adj Close 0\n",
"Volume 0\n",
"Difference 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(df.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1992-06-26</td>\n",
" <td>0.328125</td>\n",
" <td>0.347656</td>\n",
" <td>0.320313</td>\n",
" <td>0.335938</td>\n",
" <td>0.260703</td>\n",
" <td>224358400</td>\n",
" <td>0.027343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1992-06-29</td>\n",
" <td>0.339844</td>\n",
" <td>0.367188</td>\n",
" <td>0.332031</td>\n",
" <td>0.359375</td>\n",
" <td>0.278891</td>\n",
" <td>58732800</td>\n",
" <td>0.035157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-06-30</td>\n",
" <td>0.367188</td>\n",
" <td>0.371094</td>\n",
" <td>0.343750</td>\n",
" <td>0.347656</td>\n",
" <td>0.269797</td>\n",
" <td>34777600</td>\n",
" <td>0.027344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1992-07-01</td>\n",
" <td>0.351563</td>\n",
" <td>0.359375</td>\n",
" <td>0.339844</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>18316800</td>\n",
" <td>0.019531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992-07-02</td>\n",
" <td>0.359375</td>\n",
" <td>0.359375</td>\n",
" <td>0.347656</td>\n",
" <td>0.355469</td>\n",
" <td>0.275860</td>\n",
" <td>13996800</td>\n",
" <td>0.011719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8031</th>\n",
" <td>2024-05-17</td>\n",
" <td>75.269997</td>\n",
" <td>78.000000</td>\n",
" <td>74.919998</td>\n",
" <td>77.849998</td>\n",
" <td>77.849998</td>\n",
" <td>14436500</td>\n",
" <td>3.080002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8032</th>\n",
" <td>2024-05-20</td>\n",
" <td>77.680000</td>\n",
" <td>78.320000</td>\n",
" <td>76.709999</td>\n",
" <td>77.540001</td>\n",
" <td>77.540001</td>\n",
" <td>11183800</td>\n",
" <td>1.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8033</th>\n",
" <td>2024-05-21</td>\n",
" <td>77.559998</td>\n",
" <td>78.220001</td>\n",
" <td>77.500000</td>\n",
" <td>77.720001</td>\n",
" <td>77.720001</td>\n",
" <td>8916600</td>\n",
" <td>0.720001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8034</th>\n",
" <td>2024-05-22</td>\n",
" <td>77.699997</td>\n",
" <td>81.019997</td>\n",
" <td>77.440002</td>\n",
" <td>80.720001</td>\n",
" <td>80.720001</td>\n",
" <td>22063400</td>\n",
" <td>3.579995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8035</th>\n",
" <td>2024-05-23</td>\n",
" <td>80.099998</td>\n",
" <td>80.699997</td>\n",
" <td>79.169998</td>\n",
" <td>79.260002</td>\n",
" <td>79.260002</td>\n",
" <td>4651418</td>\n",
" <td>1.529999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8036 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 1992-06-26 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"1 1992-06-29 0.339844 0.367188 0.332031 0.359375 0.278891 \n",
"2 1992-06-30 0.367188 0.371094 0.343750 0.347656 0.269797 \n",
"3 1992-07-01 0.351563 0.359375 0.339844 0.355469 0.275860 \n",
"4 1992-07-02 0.359375 0.359375 0.347656 0.355469 0.275860 \n",
"... ... ... ... ... ... ... \n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998 77.849998 \n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001 77.540001 \n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001 77.720001 \n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume Difference \n",
"0 224358400 0.027343 \n",
"1 58732800 0.035157 \n",
"2 34777600 0.027344 \n",
"3 18316800 0.019531 \n",
"4 13996800 0.011719 \n",
"... ... ... \n",
"8031 14436500 3.080002 \n",
"8032 11183800 1.610001 \n",
"8033 8916600 0.720001 \n",
"8034 22063400 3.579995 \n",
"8035 4651418 1.529999 \n",
"\n",
"[8036 rows x 8 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"10. Заполнение пустых значений на основе существующих данных"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.fillna(df.mean(), inplace=True)\n",
"df.fillna(df.median(), inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Возможности визуализации**"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Линейная диаграмма\n",
"plt.figure(figsize=(10, 5))\n",
"df['High'].plot(title='Линейная диаграмма (столбец High)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Гистограмма\n",
"plt.figure(figsize=(8, 5))\n",
"df.plot.hist(column=[\"Open\"], bins=80)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 4000x1000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Столбчатая диаграмма\n",
"plt.figure(figsize=(40, 10))\n",
"df['Open'].value_counts().plot(kind='bar', title='Столбчатая диаграмма (Open)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x2000 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Ящик с усами\n",
"plt.figure(figsize=(8, 20))\n",
"df[\"Volume\"].plot(kind = \"box\", title='Ящик с усами')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Диаграмма с областями\n",
"plt.figure(figsize=(8, 5))\n",
"df[['Open', 'High']].plot(kind='area', alpha=0.2, title='Area Plot (Open, High)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Open', ylabel='Volume'>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Диаграмма рассеяния\n",
"df.plot.scatter(x=\"Open\", y=\"Volume\")"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Круговая диаграмма\n",
"\n",
"df['ForPieChart'] = df['Volume'] % 500 #Дополнительный столбец для демонстрации диаграммы\n",
"\n",
"plt.figure(figsize=(8, 5))\n",
"df['ForPieChart'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Pie Chart (Volume)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 400\n",
"1 300\n",
"2 100\n",
"3 300\n",
"4 300\n",
" ... \n",
"8031 0\n",
"8032 300\n",
"8033 100\n",
"8034 400\n",
"8035 418\n",
"Name: ForPieChart, Length: 8036, dtype: int64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['ForPieChart']"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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